Question 831 of 988

Implement knowledge mining and document intelligence solutions →easyMultiple ChoiceObjective-mapped

Quick Answer

The answer is that the Entity Recognition skill is missing a configured defaultLanguageCode, and the documents are not in English. This is correct because the Entity Recognition skill relies on the defaultLanguageCode parameter to determine the language model for entity extraction; without it, the skill defaults to English and will fail to identify organizations in non-English text, leaving the 'organizations' field empty. On the Azure AI Engineer Associate AI-102 exam, this scenario tests your understanding of how language configuration impacts skill output, often appearing as a trap where candidates assume the pipeline is broken when it is actually a language mismatch. A key memory tip is to remember that Key Phrases works across languages without explicit configuration, but Entity Recognition demands a defaultLanguageCode for non-English content—think “Entities need explicit language, phrases do not.”

AI-102 Practice Question: Implement knowledge mining and document intelligence solutions

This AI-102 practice question tests your understanding of implement knowledge mining and document intelligence solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

Exhibit

Refer to the exhibit.
```json
{
  "skills": [
    {
      "@odata.type": "#Microsoft.Skills.Text.EntityRecognitionSkill",
      "name": "#1",
      "description": "Extract organizations",
      "context": "/document",
      "categories": ["Organization"],
      "inputs": [
        {
          "name": "text",
          "source": "/document/content"
        }
      ],
      "outputs": [
        {
          "name": "organizations",
          "targetName": "organizations"
        }
      ]
    },
    {
      "@odata.type": "#Microsoft.Skills.Text.KeyPhraseExtractionSkill",
      "name": "#2",
      "description": "Extract key phrases",
      "context": "/document",
      "inputs": [
        {
          "name": "text",
          "source": "/document/content"
        }
      ],
      "outputs": [
        {
          "name": "keyPhrases",
          "targetName": "keyPhrases"
        }
      ]
    }
  ]
}
```

Refer to the exhibit. You have a skillset with two skills. You run the indexer and find that the output field 'organizations' is empty for documents that clearly contain organization names. The 'keyPhrases' output is populated correctly. What is the most likely cause of the issue?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

Question 1easymultiple choice

Full question →

Exhibit

Refer to the exhibit.
```json
{
  "skills": [
    {
      "@odata.type": "#Microsoft.Skills.Text.EntityRecognitionSkill",
      "name": "#1",
      "description": "Extract organizations",
      "context": "/document",
      "categories": ["Organization"],
      "inputs": [
        {
          "name": "text",
          "source": "/document/content"
        }
      ],
      "outputs": [
        {
          "name": "organizations",
          "targetName": "organizations"
        }
      ]
    },
    {
      "@odata.type": "#Microsoft.Skills.Text.KeyPhraseExtractionSkill",
      "name": "#2",
      "description": "Extract key phrases",
      "context": "/document",
      "inputs": [
        {
          "name": "text",
          "source": "/document/content"
        }
      ],
      "outputs": [
        {
          "name": "keyPhrases",
          "targetName": "keyPhrases"
        }
      ]
    }
  ]
}
```

A
The skill's 'name' property is set to '#1', which is invalid.
Why wrong: The name can be any string; '#1' is allowed.
B
The skill is not configured with a 'defaultLanguageCode' and the documents are not in English.
EntityRecognitionSkill needs a language hint to perform correctly for non-English languages.
C
The 'categories' property is misspelled; it should be 'entityCategories'.
Why wrong: The property 'categories' is correct for EntityRecognitionSkill.
D
The input source '/document/content' is incorrect; it should be '/document/text'.
Why wrong: The input source depends on the document parsing; '/document/content' is a common field.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The skill is not configured with a 'defaultLanguageCode' and the documents are not in English.

The 'keyPhrases' output is populated correctly, indicating the text extraction and overall pipeline are functional. The Entity Recognition skill requires a 'defaultLanguageCode' to correctly identify entities; if it is not set and the documents are not in English, the skill may fail to extract organizations, resulting in an empty 'organizations' field. This is a known behavior where the skill defaults to English and cannot process other languages without explicit configuration.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The skill's 'name' property is set to '#1', which is invalid.
Why it's wrong here
The name can be any string; '#1' is allowed.
✓
The skill is not configured with a 'defaultLanguageCode' and the documents are not in English.
Why this is correct
EntityRecognitionSkill needs a language hint to perform correctly for non-English languages.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The 'categories' property is misspelled; it should be 'entityCategories'.
Why it's wrong here
The property 'categories' is correct for EntityRecognitionSkill.
✗
The input source '/document/content' is incorrect; it should be '/document/text'.
Why it's wrong here
The input source depends on the document parsing; '/document/content' is a common field.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume the 'keyPhrases' skill working correctly implies all skills are fine, overlooking that Entity Recognition is language-dependent and requires explicit 'defaultLanguageCode' configuration, while 'keyPhrases' is more robust across languages.

Detailed technical explanation

How to think about this question

The Entity Recognition skill in Azure Cognitive Search uses a language detection model that relies on the 'defaultLanguageCode' to determine the linguistic context for entity extraction. If this property is omitted, the skill assumes English, and for non-English text, it may return empty results for specific entity categories like 'organizations' while still extracting 'keyPhrases' (which uses a different, more language-agnostic algorithm). This behavior is documented in the skill reference, where language-dependent skills require explicit language configuration to function correctly across multilingual datasets.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-102 question test?

Implement knowledge mining and document intelligence solutions — This question tests Implement knowledge mining and document intelligence solutions — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The skill is not configured with a 'defaultLanguageCode' and the documents are not in English. — The 'keyPhrases' output is populated correctly, indicating the text extraction and overall pipeline are functional. The Entity Recognition skill requires a 'defaultLanguageCode' to correctly identify entities; if it is not set and the documents are not in English, the skill may fail to extract organizations, resulting in an empty 'organizations' field. This is a known behavior where the skill defaults to English and cannot process other languages without explicit configuration.

What should I do if I get this AI-102 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 11, 2026

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